Value-Free Policy Optimization via Reward Partitioning 事件

Value-Free Policy Optimization via Reward Partitioning arXiv:2506.13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback. Existing approaches such as Direct Reward Optimization (DRO) have demonstrated promising results but rely on value function estimation, introducing additional variance, op